On the internal evaluation of unsupervised outlier detection

Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek, Jörg Sander

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

Although there is a large and growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results is still virtually untouched in the literature. The so-called internal evaluation, based solely on the data and the assessed solutions themselves, is required if one wants to statistically validate (in absolute terms) or just compare (in relative terms) the solutions provided by different algorithms or by different parameterizations of a given algorithm in the absence of labeled data. However, in contrast to unsupervised cluster analysis, where indexes for internal evaluation and validation of clustering solutions have been conceived and shown to be very useful, in the outlier detection domain this problem has been notably overlooked. Here we discuss this problem and provide a solution for the internal evaluation of top-n (binary) outlier detection results. Specifically, we propose an index called IREOS (Internal, Relative Evaluation of Outlier Solutions) that can evaluate and compare different candidate labelings of a collection of multivariate observations in terms of outliers and inliers. We also statistically adjust IREOS for chance and extensively evaluate it in several experiments involving different collections of synthetic and real data sets.

Original languageEnglish
Title of host publicationProceedings of the 27th International Conference on Scientific and Statistical Database Management, SSDBM '15, La Jolla, CA, USA, June 29 - July 1, 2015
EditorsAmarnath Gupta, Susan L. Rathbun
Number of pages12
PublisherAssociation for Computing Machinery
Publication date2015
Pages1-12
Article number7
DOIs
Publication statusPublished - 2015

Keywords

  • Outlier detection
  • Unsupervised evaluation
  • Validation

Fingerprint

Dive into the research topics of 'On the internal evaluation of unsupervised outlier detection'. Together they form a unique fingerprint.

Cite this